How to build mobile Robot

how to control mobile robot and how to make a mobile robot at home and how to make mobile operated robot vehicle and how to make mobile operated robot
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Dr.NaveenBansal,India,Teacher
Published Date:25-10-2017
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1 Introduction 1.1 Introduction Robotics has achieved its greatest success to date in the world of industrial manufacturing. Robot arms, or manipulators, comprise a 2 billion dollar industry. Bolted at its shoulder to a specific position in the assembly line, the robot arm can move with great speed and accu- racy to perform repetitive tasks such as spot welding and painting (figure 1.1). In the elec- tronics industry, manipulators place surface-mounted components with superhuman precision, making the portable telephone and laptop computer possible. Yet, for all of their successes, these commercial robots suffer from a fundamental dis- advantage: lack of mobility. A fixed manipulator has a limited range of motion that depends © KUKA Inc. © SIG Demaurex SA Figure 1.1 Picture of auto assembly plant-spot welding robot of KUKA and a parallel robot Delta of SIG Demau- rex SA (invented at EPFL 140) during packaging of chocolates. 2 Chapter 1 on where it is bolted down. In contrast, a mobile robot would be able to travel throughout the manufacturing plant, flexibly applying its talents wherever it is most effective. This book focuses on the technology of mobility: how can a mobile robot move unsu- pervised through real-world environments to fulfill its tasks? The first challenge is locomo- tion itself. How should a mobile robot move, and what is it about a particular locomotion mechanism that makes it superior to alternative locomotion mechanisms? Hostile environments such as Mars trigger even more unusual locomotion mechanisms (figure 1.2). In dangerous and inhospitable environments, even on Earth, such teleoperated systems have gained popularity (figures 1.3, 1.4, 1.5, 1.6). In these cases, the low-level complexities of the robot often make it impossible for a human operator to directly control its motions. The human performs localization and cognition activities, but relies on the robot’s control scheme to provide motion control. For example, Plustech’s walking robot provides automatic leg coordination while the human operator chooses an overall direction of travel (figure 1.3). Figure 1.6 depicts an underwater vehicle that controls six propellers to autonomously stabilize the robot subma- rine in spite of underwater turbulence and water currents while the operator chooses posi- tion goals for the submarine to achieve. Other commercial robots operate not where humans cannot go but rather share space with humans in human environments (figure 1.7). These robots are compelling not for rea- sons of mobility but because of their autonomy, and so their ability to maintain a sense of position and to navigate without human intervention is paramount. Figure 1.2 The mobile robot Sojourner was used during the Pathfinder mission to explore Mars in summer 1997. It was almost completely teleoperated from Earth. However, some on-board sensors allowed for obstacle detection. (http://ranier.oact.hq.nasa.gov/telerobotics_page/telerobotics.shtm). © NASA/JPLIntroduction 3 Figure 1.3 Plustech developed the first application-driven walking robot. It is designed to move wood out of the forest. The leg coordination is automated, but navigation is still done by the human operator on the robot. (http://www.plustech.fi). © Plustech. Figure 1.4 Airduct inspection robot featuring a pan-tilt camera with zoom and sensors for automatic inclination control, wall following, and intersection detection (http://asl.epfl.ch). © Sedirep / EPFL.4 Chapter 1 Figure 1.5 Picture of Pioneer, a robot designed to explore the Sarcophagus at Chernobyl. © Wide World Photos. Figure 1.6 Picture of recovering MBARI’s ALTEX AUV (autonomous underwater vehicle) onto the Icebreaker Healy following a dive beneath the Arctic ice. Todd Walsh © 2001 MBARI.Introduction 5 Figure 1.7 Tour-guide robots are able to interact and present exhibitions in an educational way 48, 118, 132, 143,. Ten Roboxes have operated during 5 months at the Swiss exhibition EXPO.02, meeting hun- dreds of thousands of visitors. They were developed by EPFL 132 (http://robotics.epfl.ch) and com- mercialized by BlueBotics (http://www.bluebotics.ch). Figure 1.8 Newest generation of the autonomous guided vehicle (AGV) of SWISSLOG used to transport motor blocks from one assembly station to another. It is guided by an electrical wire installed in the floor. There are thousands of AGVs transporting products in industry, warehouses, and even hospitals. © Swisslog.6 Chapter 1 front back Figure 1.9 HELPMATE is a mobile robot used in hospitals for transportation tasks. It has various on-board sen- sors for autonomous navigation in the corridors. The main sensor for localization is a camera looking to the ceiling. It can detect the lamps on the ceiling as references, or landmarks (http:// www.pyxis.com). © Pyxis Corp. Figure 1.10 BR 700 industrial cleaning robot (left) and the RoboCleaner RC 3000 consumer robot developed and sold by Alfred Kärcher GmbH & Co., Germany. The navigation system of BR 700 is based on a very sophisticated sonar system and a gyro. The RoboCleaner RC 3000 covers badly soiled areas with a special driving strategy until it is really clean. Optical sensors measure the degree of pollution of the aspirated air (http://www.karcher.de). © Alfred Kärcher GmbH & Co.Introduction 7 Figure 1.11 PIONEER is a modular mobile robot offering various options like a gripper or an on-board camera. It is equipped with a sophisticated navigation library developed at SRI, Stanford, CA (Reprinted with permission from ActivMedia Robotics, http://www.MobileRobots.com). Figure 1.12 B21 of iRobot is a sophisticated mobile robot with up to three Intel Pentium processors on board. It has a large variety of sensors for high-performance navigation tasks (http://www.irobot.com/rwi/). © iRobot Inc.8 Chapter 1 Figure 1.13 KHEPERA is a small mobile robot for research and education. It is only about 60 mm in diameter. Various additional modules such as cameras and grippers are available. More then 700 units had already been sold by the end of 1998. KHEPERA is manufactured and distributed by K-Team SA, Switzerland (http://www.k-team.com). © K-Team SA. For example, AGV (autonomous guided vehicle) robots (figure 1.8) autonomously deliver parts between various assembly stations by following special electrical guidewires using a custom sensor. The Helpmate service robot transports food and medication throughout hospitals by tracking the position of ceiling lights, which are manually specified to the robot beforehand (figure 1.9). Several companies have developed autonomous clean- ing robots, mainly for large buildings (figure 1.10). One such cleaning robot is in use at the Paris Metro. Other specialized cleaning robots take advantage of the regular geometric pat- tern of aisles in supermarkets to facilitate the localization and navigation tasks. Research into high-level questions of cognition, localization, and navigation can be per- formed using standard research robot platforms that are tuned to the laboratory environ- ment. This is one of the largest current markets for mobile robots. Various mobile robot platforms are available for programming, ranging in terms of size and terrain capability. The most popular research robots are those of ActivMedia Robotics, K-Team SA, and I- Robot (figures 1.11, 1.12, 1.13) and also very small robots like the Alice from EPFL (Swiss Federal Institute of Technology at Lausanne) (figure 1.14). Although mobile robots have a broad set of applications and markets as summarized above, there is one fact that is true of virtually every successful mobile robot: its design involves the integration of many different bodies of knowledge. No mean feat, this makes mobile robotics as interdisciplinary a field as there can be. To solve locomotion problems, the mobile roboticist must understand mechanism and kinematics; dynamics and control theory. To create robust perceptual systems, the mobile roboticist must leverage the fields of signal analysis and specialized bodies of knowledge such as computer vision to properlyIntroduction 9 employ a multitude of sensor technologies. Localization and navigation demand knowl- edge of computer algorithms, information theory, artificial intelligence, and probability theory. Figure 1.15 depicts an abstract control scheme for mobile robot systems that we will use throughout this text. This figure identifies many of the main bodies of knowledge associ- ated with mobile robotics. This book provides an introduction to all aspects of mobile robotics, including software and hardware design considerations, related technologies, and algorithmic techniques. The intended audience is broad, including both undergraduate and graduate students in intro- ductory mobile robotics courses, as well as individuals fascinated by the field. While not absolutely required, a familiarity with matrix algebra, calculus, probability theory, and computer programming will significantly enhance the reader’s experience. Mobile robotics is a large field, and this book focuses not on robotics in general, nor on mobile robot applications, but rather on mobility itself. From mechanism and perception to localization and navigation, this book focuses on the techniques and technologies that enable robust mobility. Clearly, a useful, commercially viable mobile robot does more than just move. It pol- ishes the supermarket floor, keeps guard in a factory, mows the golf course, provides tours in a museum, or provides guidance in a supermarket. The aspiring mobile roboticist will start with this book, but quickly graduate to course work and research specific to the desired application, integrating techniques from fields as disparate as human-robot interaction, computer vision, and speech understanding. Figure 1.14 Alice is one of the smallest fully autonomous robots. It is approximately 2x2x2 cm, it has an auton- omy of about 8 hours and uses infrared distance sensors, tactile whiskers, or even a small camera for navigation 54. 10 Chapter 1 Knowledge, Mission Data Base Commands Localization Cognition “Position” Map Building Path Planing Global Map Environment Model Path Local Map Information Path Extraction and Execution Interpretation Raw data Actuator Commands Sensing Acting Real World Environment Figure 1.15 Reference control scheme for mobile robot systems used throughout this book. 1.2 An Overview of the Book This book introduces the different aspects of a robot in modules, much like the modules shown in figure 1.15. Chapters 2 and 3 focus on the robot’s low-level locomotive ability. Chapter 4 presents an in-depth view of perception. Then, Chapters 5 and 6 take us to the higher-level challenges of localization and even higher-level cognition, specifically the ability to navigate robustly. Each chapter builds upon previous chapters, and so the reader is encouraged to start at the beginning, even if their interest is primarily at the high level. Robotics is peculiar in that solutions to high-level challenges are most meaningful only in the context of a solid understanding of the low-level details of the system. Chapter 2, “Locomotion”, begins with a survey of the most popular mechanisms that enable locomotion: wheels and legs. Numerous robotic examples demonstrate the particu- Perception Motion ControlIntroduction 11 lar talents of each form of locomotion. But designing a robot’s locomotive system properly requires the ability to evaluate its overall motion capabilities quantitatively. Chapter 3, “Mobile Robot Kinematics”, applies principles of kinematics to the whole robot, beginning with the kinematic contribution of each wheel and graduating to an analysis of robot maneuverability enabled by each mobility mechanism configuration. The greatest single shortcoming in conventional mobile robotics is, without doubt, per- ception: mobile robots can travel across much of earth’s man-made surfaces, but they cannot perceive the world nearly as well as humans and other animals. Chapter 4, “Percep- tion”, begins a discussion of this challenge by presenting a clear language for describing the performance envelope of mobile robot sensors. With this language in hand, chapter 4 goes on to present many of the off-the-shelf sensors available to the mobile roboticist, describing their basic principles of operation as well as their performance limitations. The most promising sensor for the future of mobile robotics is vision, and chapter 4 includes an overview of the theory of operation and the limitations of both charged coupled device (CCD) and complementary metal oxide semiconductor (CMOS) sensors. But perception is more than sensing. Perception is also the interpretation of sensed data in meaningful ways. The second half of chapter 4 describes strategies for feature extraction that have been most useful in mobile robotics applications, including extraction of geomet- ric shapes from range-based sensing data, as well as landmark and whole-image analysis using vision-based sensing. Armed with locomotion mechanisms and outfitted with hardware and software for per- ception, the mobile robot can move and perceive the world. The first point at which mobil- ity and sensing must meet is localization: mobile robots often need to maintain a sense of position. Chapter 5, “Mobile Robot Localization”, describes approaches that obviate the need for direct localization, then delves into fundamental ingredients of successful local- ization strategies: belief representation and map representation. Case studies demonstrate various localization schemes, including both Markov localization and Kalman filter local- ization. The final part of chapter 5 is devoted to a discussion of the challenges and most promising techniques for mobile robots to autonomously map their surroundings. Mobile robotics is so young a discipline that it lacks a standardized architecture. There is as yet no established robot operating system. But the question of architecture is of para- mount importance when one chooses to address the higher-level competences of a mobile robot: how does a mobile robot navigate robustly from place to place, interpreting data, localizing and controlling its motion all the while? For this highest level of robot compe- tence, which we term navigation competence, there are numerous mobile robots that show- case particular architectural strategies. Chapter 6, “Planning and Navigation”, surveys the state of the art of robot navigation, showing that today’s various techniques are quite sim- ilar, differing primarily in the manner in which they decompose the problem of robot con-12 Chapter 1 trol. But first, chapter 6 addresses two skills that a competent, navigating robot usually must demonstrate: obstacle avoidance and path planning. There is far more to know about the cross-disciplinary field of mobile robotics than can be contained in a single book. We hope, though, that this broad introduction will place the reader in the context of mobile robotics’ collective wisdom. This is only the beginning, but, with luck, the first robot you program or build will have only good things to say about you.2 Locomotion 2.1 Introduction A mobile robot needs locomotion mechanisms that enable it to move unbounded through- out its environment. But there are a large variety of possible ways to move, and so the selec- tion of a robot’s approach to locomotion is an important aspect of mobile robot design. In the laboratory, there are research robots that can walk, jump, run, slide, skate, swim, fly, and, of course, roll. Most of these locomotion mechanisms have been inspired by their bio- logical counterparts (see figure 2.1). There is, however, one exception: the actively powered wheel is a human invention that achieves extremely high efficiency on flat ground. This mechanism is not completely for- eign to biological systems. Our bipedal walking system can be approximated by a rolling polygon, with sides equal in length d to the span of the step (figure 2.2). As the step size decreases, the polygon approaches a circle or wheel. But nature did not develop a fully rotating, actively powered joint, which is the technology necessary for wheeled locomo- tion. Biological systems succeed in moving through a wide variety of harsh environments. Therefore it can be desirable to copy their selection of locomotion mechanisms. However, replicating nature in this regard is extremely difficult for several reasons. To begin with, mechanical complexity is easily achieved in biological systems through structural replica- tion. Cell division, in combination with specialization, can readily produce a millipede with several hundred legs and several tens of thousands of individually sensed cilia. In man- made structures, each part must be fabricated individually, and so no such economies of scale exist. Additionally, the cell is a microscopic building block that enables extreme min- iaturization. With very small size and weight, insects achieve a level of robustness that we have not been able to match with human fabrication techniques. Finally, the biological energy storage system and the muscular and hydraulic activation systems used by large ani- mals and insects achieve torque, response time, and conversion efficiencies that far exceed similarly scaled man-made systems. 14 Chapter 2 Type of motion Resistance to motion Basic kinematics of motion Flow in a Channel Hydrodynamic forces Eddies Crawl Friction forces Longitudinal vibration Friction forces Transverse vibration Sliding Oscillatory movement of a multi-link Loss of kinetic energy Running pendulum Oscillatory movement of a multi-link Jumping Loss of kinetic energy pendulum Rolling of a polygon Walking Gravitational forces (see figure 2.2) Figure 2.1 Locomotion mechanisms used in biological systems. Owing to these limitations, mobile robots generally locomote either using wheeled mechanisms, a well-known human technology for vehicles, or using a small number of articulated legs, the simplest of the biological approaches to locomotion (see figure 2.2). In general, legged locomotion requires higher degrees of freedom and therefore greater mechanical complexity than wheeled locomotion. Wheels, in addition to being simple, are extremely well suited to flat ground. As figure 2.3 depicts, on flat surfaces wheeled loco- motion is one to two orders of magnitude more efficient than legged locomotion. The rail- way is ideally engineered for wheeled locomotion because rolling friction is minimized on a hard and flat steel surface. But as the surface becomes soft, wheeled locomotion accumu- lates inefficiencies due to rolling friction whereas legged locomotion suffers much less because it consists only of point contacts with the ground. This is demonstrated in figure 2.3 by the dramatic loss of efficiency in the case of a tire on soft ground.Locomotion 15 h O l αα d Figure 2.2 A biped walking system can be approximated by a rolling polygon, with sides equal in length d to the span of the step. As the step size decreases, the polygon approaches a circle or wheel with the radius l. 100 10 1 0.1 1 10 100 speed (miles/hour) Figure 2.3 Specific power versus attainable speed of various locomotion mechanisms 33. crawling/sliding running tire on soft ground walking railway wheel flow unit power (hp/ton)16 Chapter 2 Figure 2.4 RoboTrac, a hybrid wheel-leg vehicle for rough terrain 130. In effect, the efficiency of wheeled locomotion depends greatly on environmental qual- ities, particularly the flatness and hardness of the ground, while the efficiency of legged locomotion depends on the leg mass and body mass, both of which the robot must support at various points in a legged gait. It is understandable therefore that nature favors legged locomotion, since locomotion systems in nature must operate on rough and unstructured terrain. For example, in the case of insects in a forest the vertical variation in ground height is often an order of magnitude greater than the total height of the insect. By the same token, the human environment fre- quently consists of engineered, smooth surfaces, both indoors and outdoors. Therefore, it is also understandable that virtually all industrial applications of mobile robotics utilize some form of wheeled locomotion. Recently, for more natural outdoor environments, there has been some progress toward hybrid and legged industrial robots such as the forestry robot shown in figure 2.4. In the section 2.1.1, we present general considerations that concern all forms of mobile robot locomotion. Following this, in sections 2.2 and 2.3, we present overviews of legged locomotion and wheeled locomotion techniques for mobile robots. 2.1.1 Key issues for locomotion Locomotion is the complement of manipulation. In manipulation, the robot arm is fixed but moves objects in the workspace by imparting force to them. In locomotion, the environ- ment is fixed and the robot moves by imparting force to the environment. In both cases, the scientific basis is the study of actuators that generate interaction forces, and mechanismsLocomotion 17 that implement desired kinematic and dynamic properties. Locomotion and manipulation thus share the same core issues of stability, contact characteristics, and environmental type: • stability - number and geometry of contact points - center of gravity - static/dynamic stability - inclination of terrain • characteristics of contact - contact point/path size and shape - angle of contact - friction • type of environment - structure - medium, (e.g. water, air, soft or hard ground) A theoretical analysis of locomotion begins with mechanics and physics. From this start- ing point, we can formally define and analyze all manner of mobile robot locomotion sys- tems. However, this book focuses on the mobile robot navigation problem, particularly stressing perception, localization, and cognition. Thus we will not delve deeply into the physical basis of locomotion. Nevertheless, the two remaining sections in this chapter present overviews of issues in legged locomotion 33 and wheeled locomotion. Then, chapter 3 presents a more detailed analysis of the kinematics and control of wheeled mobile robots. 2.2 Legged Mobile Robots Legged locomotion is characterized by a series of point contacts between the robot and the ground. The key advantages include adaptability and maneuverability in rough terrain. Because only a set of point contacts is required, the quality of the ground between those points does not matter so long as the robot can maintain adequate ground clearance. In addi- tion, a walking robot is capable of crossing a hole or chasm so long as its reach exceeds the width of the hole. A final advantage of legged locomotion is the potential to manipulate objects in the environment with great skill. An excellent insect example, the dung beetle, is capable of rolling a ball while locomoting by way of its dexterous front legs. The main disadvantages of legged locomotion include power and mechanical complex- ity. The leg, which may include several degrees of freedom, must be capable of sustaining part of the robot’s total weight, and in many robots must be capable of lifting and lowering the robot. Additionally, high maneuverability will only be achieved if the legs have a suf- ficient number of degrees of freedom to impart forces in a number of different directions.18 Chapter 2 mammals reptiles insects two or four legs four legs six legs Figure 2.5 Arrangement of the legs of various animals. 2.2.1 Leg configurations and stability Because legged robots are biologically inspired, it is instructive to examine biologically successful legged systems. A number of different leg configurations have been successful in a variety of organisms (figure 2.5). Large animals, such as mammals and reptiles, have four legs, whereas insects have six or more legs. In some mammals, the ability to walk on only two legs has been perfected. Especially in the case of humans, balance has progressed 1 to the point that we can even jump with one leg . This exceptional maneuverability comes at a price: much more complex active control to maintain balance. In contrast, a creature with three legs can exhibit a static, stable pose provided that it can ensure that its center of gravity is within the tripod of ground contact. Static stability, dem- onstrated by a three-legged stool, means that balance is maintained with no need for motion. A small deviation from stability (e.g., gently pushing the stool) is passively cor- rected toward the stable pose when the upsetting force stops. But a robot must be able to lift its legs in order to walk. In order to achieve static walk- ing, a robot must have at least six legs. In such a configuration, it is possible to design a gait in which a statically stable tripod of legs is in contact with the ground at all times (figure 2.8). Insects and spiders are immediately able to walk when born. For them, the problem of balance during walking is relatively simple. Mammals, with four legs, cannot achieve static walking, but are able to stand easily on four legs. Fauns, for example, spend several minutes attempting to stand before they are able to do so, then spend several more minutes learning to walk without falling. Humans, with two legs, cannot even stand in one place with static stability. Infants require months to stand and walk, and even longer to learn to jump, run, and stand on one leg. 1. In child development, one of the tests used to determine if the child is acquiring advanced loco- motion skills is the ability to jump on one leg.Locomotion 19 abduction-adduction hip abduction angle (θ) θ knee flexion angle (ϕ) lift ϕ upper thigh link ψ main drive hip flexion angle (ψ) lower thigh link shank link Figure 2.6 Two examples of legs with three degrees of freedom. There is also the potential for great variety in the complexity of each individual leg. Once again, the biological world provides ample examples at both extremes. For instance, in the case of the caterpillar, each leg is extended using hydraulic pressure by constricting the body cavity and forcing an increase in pressure, and each leg is retracted longitudinally by relaxing the hydraulic pressure, then activating a single tensile muscle that pulls the leg in toward the body. Each leg has only a single degree of freedom, which is oriented longi- tudinally along the leg. Forward locomotion depends on the hydraulic pressure in the body, which extends the distance between pairs of legs. The caterpillar leg is therefore mechani- cally very simple, using a minimal number of extrinsic muscles to achieve complex overall locomotion. At the other extreme, the human leg has more than seven major degrees of freedom, combined with further actuation at the toes. More than fifteen muscle groups actuate eight complex joints. In the case of legged mobile robots, a minimum of two degrees of freedom is generally required to move a leg forward by lifting the leg and swinging it forward. More common is the addition of a third degree of freedom for more complex maneuvers, resulting in legs such as those shown in figure 2.6. Recent successes in the creation of bipedal walking robots have added a fourth degree of freedom at the ankle joint. The ankle enables more consistent ground contact by actuating the pose of the sole of the foot. In general, adding degrees of freedom to a robot leg increases the maneuverability of the robot, both augmenting the range of terrains on which it can travel and the ability of the robot to travel with a variety of gaits. The primary disadvantages of additional joints and actuators are, of course, energy, control, and mass. Additional actuators require energy and control, and they also add to leg mass, further increasing power and load requirements on existing actuators.20 Chapter 2 free fly changeover walking galloping Figure 2.7 Two gaits with four legs. Because this robot has fewer than six legs, static walking is not generally possible. In the case of a multilegged mobile robot, there is the issue of leg coordination for loco- motion, or gait control. The number of possible gaits depends on the number of legs 33. The gait is a sequence of lift and release events for the individual legs. For a mobile robot with k legs, the total number of possible events N for a walking machine is N =() 2k – 1 (2.1) For a biped walker k = 2 legs, the number of possible events N is N== () 2k – 133= ⋅⋅21= 6 (2.2)

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